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<div class="header">
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<a href="#pub-methods">Public 成员函数</a> &#124;
<a href="#pro-types">Protected 类型</a> &#124;
<a href="#pri-attribs">Private 属性</a> &#124;
<a href="classpcl_1_1_conditional_euclidean_clustering-members.html">所有成员列表</a>  </div>
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<div class="title">pcl::ConditionalEuclideanClustering&lt; PointT &gt; 模板类 参考</div>  </div>
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<p><b><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html" title="ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined c...">ConditionalEuclideanClustering</a></b> performs segmentation based on Euclidean distance and a user-defined clustering condition.  
 <a href="classpcl_1_1_conditional_euclidean_clustering.html#details">更多...</a></p>

<p><code>#include &lt;<a class="el" href="conditional__euclidean__clustering_8h_source.html">conditional_euclidean_clustering.h</a>&gt;</code></p>
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类 pcl::ConditionalEuclideanClustering&lt; PointT &gt; 继承关系图:</div>
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  <img src="classpcl_1_1_conditional_euclidean_clustering.png" usemap="#pcl::ConditionalEuclideanClustering_3C_20PointT_20_3E_map" alt=""/>
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<area href="classpcl_1_1_p_c_l_base.html" title="PCL base class. Implements methods that are used by most PCL algorithms." alt="pcl::PCLBase&lt; PointT &gt;" shape="rect" coords="0,0,270,24"/>
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<table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pub-methods"></a>
Public 成员函数</h2></td></tr>
<tr class="memitem:af707af3b42a9b8cd4314e95ac9134eed"><td class="memItemLeft" align="right" valign="top">&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#af707af3b42a9b8cd4314e95ac9134eed">ConditionalEuclideanClustering</a> (bool extract_removed_clusters=false)</td></tr>
<tr class="memdesc:af707af3b42a9b8cd4314e95ac9134eed"><td class="mdescLeft">&#160;</td><td class="mdescRight">Constructor.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#af707af3b42a9b8cd4314e95ac9134eed">更多...</a><br /></td></tr>
<tr class="separator:af707af3b42a9b8cd4314e95ac9134eed"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a62bf691a0260fc34c3df9d9d48cb2faa"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa">setConditionFunction</a> (bool(*condition_function)(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, float))</td></tr>
<tr class="memdesc:a62bf691a0260fc34c3df9d9d48cb2faa"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the condition that needs to hold for neighboring points to be considered part of the same cluster.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa">更多...</a><br /></td></tr>
<tr class="separator:a62bf691a0260fc34c3df9d9d48cb2faa"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:afc78950824bf9940d260835543862850"><td class="memItemLeft" align="right" valign="top"><a id="afc78950824bf9940d260835543862850"></a>
void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#afc78950824bf9940d260835543862850">setConditionFunction</a> (boost::function&lt; bool(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, float)&gt; condition_function)</td></tr>
<tr class="memdesc:afc78950824bf9940d260835543862850"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the condition that needs to hold for neighboring points to be considered part of the same cluster. This is an overloaded function provided for convenience. See the documentation for <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa" title="Set the condition that needs to hold for neighboring points to be considered part of the same cluster...">setConditionFunction()</a>. <br /></td></tr>
<tr class="separator:afc78950824bf9940d260835543862850"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:abc0ffbe8624cf5c990484e35055f3c87"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#abc0ffbe8624cf5c990484e35055f3c87">setClusterTolerance</a> (float cluster_tolerance)</td></tr>
<tr class="memdesc:abc0ffbe8624cf5c990484e35055f3c87"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the spatial tolerance for new cluster candidates.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#abc0ffbe8624cf5c990484e35055f3c87">更多...</a><br /></td></tr>
<tr class="separator:abc0ffbe8624cf5c990484e35055f3c87"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a462b1b1ee44fd92efd8af88add42c61a"><td class="memItemLeft" align="right" valign="top"><a id="a462b1b1ee44fd92efd8af88add42c61a"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a462b1b1ee44fd92efd8af88add42c61a">getClusterTolerance</a> ()</td></tr>
<tr class="memdesc:a462b1b1ee44fd92efd8af88add42c61a"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the spatial tolerance for new cluster candidates. <br /></td></tr>
<tr class="separator:a462b1b1ee44fd92efd8af88add42c61a"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a05a9b657c356141464cb80ee22ab53c2"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a05a9b657c356141464cb80ee22ab53c2">setMinClusterSize</a> (int min_cluster_size)</td></tr>
<tr class="memdesc:a05a9b657c356141464cb80ee22ab53c2"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the minimum number of points that a cluster needs to contain in order to be considered valid.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#a05a9b657c356141464cb80ee22ab53c2">更多...</a><br /></td></tr>
<tr class="separator:a05a9b657c356141464cb80ee22ab53c2"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ae97b076d0e5f939189c3c2207e872785"><td class="memItemLeft" align="right" valign="top"><a id="ae97b076d0e5f939189c3c2207e872785"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#ae97b076d0e5f939189c3c2207e872785">getMinClusterSize</a> ()</td></tr>
<tr class="memdesc:ae97b076d0e5f939189c3c2207e872785"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the minimum number of points that a cluster needs to contain in order to be considered valid. <br /></td></tr>
<tr class="separator:ae97b076d0e5f939189c3c2207e872785"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8c0ad8703fab1ddc50e09924eb2cfcc8"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a8c0ad8703fab1ddc50e09924eb2cfcc8">setMaxClusterSize</a> (int max_cluster_size)</td></tr>
<tr class="memdesc:a8c0ad8703fab1ddc50e09924eb2cfcc8"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the maximum number of points that a cluster needs to contain in order to be considered valid.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#a8c0ad8703fab1ddc50e09924eb2cfcc8">更多...</a><br /></td></tr>
<tr class="separator:a8c0ad8703fab1ddc50e09924eb2cfcc8"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a003e5aabe77befd122290e8039e013ec"><td class="memItemLeft" align="right" valign="top"><a id="a003e5aabe77befd122290e8039e013ec"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a003e5aabe77befd122290e8039e013ec">getMaxClusterSize</a> ()</td></tr>
<tr class="memdesc:a003e5aabe77befd122290e8039e013ec"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the maximum number of points that a cluster needs to contain in order to be considered valid. <br /></td></tr>
<tr class="separator:a003e5aabe77befd122290e8039e013ec"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8c03a476748bf42bbca42bc66141e785"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a8c03a476748bf42bbca42bc66141e785">segment</a> (IndicesClusters &amp;clusters)</td></tr>
<tr class="memdesc:a8c03a476748bf42bbca42bc66141e785"><td class="mdescLeft">&#160;</td><td class="mdescRight">Segment the input into separate clusters.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#a8c03a476748bf42bbca42bc66141e785">更多...</a><br /></td></tr>
<tr class="separator:a8c03a476748bf42bbca42bc66141e785"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af264099e843fbf5d48049857e1e1d6af"><td class="memItemLeft" align="right" valign="top">void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#af264099e843fbf5d48049857e1e1d6af">getRemovedClusters</a> (IndicesClustersPtr &amp;small_clusters, IndicesClustersPtr &amp;large_clusters)</td></tr>
<tr class="memdesc:af264099e843fbf5d48049857e1e1d6af"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get the clusters that are invalidated due to size constraints.  <a href="classpcl_1_1_conditional_euclidean_clustering.html#af264099e843fbf5d48049857e1e1d6af">更多...</a><br /></td></tr>
<tr class="separator:af264099e843fbf5d48049857e1e1d6af"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pub_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="af4fbc5eb005057f8a0fc6d60bde595df"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af4fbc5eb005057f8a0fc6d60bde595df">PCLBase</a> ()</td></tr>
<tr class="memdesc:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Empty constructor. <br /></td></tr>
<tr class="separator:af4fbc5eb005057f8a0fc6d60bde595df inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a7a6dd7a91275d7737cf1b18005b47244"></a>
&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a7a6dd7a91275d7737cf1b18005b47244">PCLBase</a> (const <a class="el" href="classpcl_1_1_p_c_l_base.html">PCLBase</a> &amp;base)</td></tr>
<tr class="memdesc:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Copy constructor. <br /></td></tr>
<tr class="separator:a7a6dd7a91275d7737cf1b18005b47244 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ad5d6846e98e59c37dcc3dc9958d53966"></a>
virtual&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ad5d6846e98e59c37dcc3dc9958d53966">~PCLBase</a> ()</td></tr>
<tr class="memdesc:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Destructor. <br /></td></tr>
<tr class="separator:ad5d6846e98e59c37dcc3dc9958d53966 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">setInputCloud</a> (const PointCloudConstPtr &amp;cloud)</td></tr>
<tr class="memdesc:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the input dataset  <a href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea">更多...</a><br /></td></tr>
<tr class="separator:a1952d7101f3942bac3b69ed55c1ca7ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a8cd745c4f7a792212f4fc3720b9d46ea"></a>
PointCloudConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a8cd745c4f7a792212f4fc3720b9d46ea">getInputCloud</a> () const</td></tr>
<tr class="memdesc:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the input point cloud dataset. <br /></td></tr>
<tr class="separator:a8cd745c4f7a792212f4fc3720b9d46ea inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">setIndices</a> (const IndicesPtr &amp;indices)</td></tr>
<tr class="memdesc:ab219359de6eb34c9d51e2e976dd1a0d1 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1">更多...</a><br /></td></tr>
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<tr class="memitem:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">setIndices</a> (const IndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#a436c68c74b31e4dd00000adfbb11ca7c">更多...</a><br /></td></tr>
<tr class="separator:a436c68c74b31e4dd00000adfbb11ca7c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="memitem:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">setIndices</a> (const PointIndicesConstPtr &amp;indices)</td></tr>
<tr class="memdesc:af9cc90d8364ce968566f75800d3773ca inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Provide a pointer to the vector of indices that represents the input data.  <a href="classpcl_1_1_p_c_l_base.html#af9cc90d8364ce968566f75800d3773ca">更多...</a><br /></td></tr>
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<tr class="memitem:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">virtual void&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">setIndices</a> (size_t row_start, size_t col_start, size_t nb_rows, size_t nb_cols)</td></tr>
<tr class="memdesc:a930c7a6375fdf65ff8cfdb4eb4a6d996 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set the indices for the points laying within an interest region of the point cloud.  <a href="classpcl_1_1_p_c_l_base.html#a930c7a6375fdf65ff8cfdb4eb4a6d996">更多...</a><br /></td></tr>
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IndicesPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a058753dd4de73d3d0062fe2e452fba3c">getIndices</a> ()</td></tr>
<tr class="memdesc:a058753dd4de73d3d0062fe2e452fba3c inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="acae187b37230758959572ceb1e6e2045"></a>
IndicesConstPtr const&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acae187b37230758959572ceb1e6e2045">getIndices</a> () const</td></tr>
<tr class="memdesc:acae187b37230758959572ceb1e6e2045 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Get a pointer to the vector of indices used. <br /></td></tr>
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<tr class="memitem:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">operator[]</a> (size_t pos) const</td></tr>
<tr class="memdesc:af7335fedb0af0930b9d1dedcb54ba201 inherit pub_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Override <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a> operator[] to shorten code  <a href="classpcl_1_1_p_c_l_base.html#af7335fedb0af0930b9d1dedcb54ba201">更多...</a><br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pro-types"></a>
Protected 类型</h2></td></tr>
<tr class="memitem:abb103ab0852677c97b78ca12a39ba930"><td class="memItemLeft" align="right" valign="top"><a id="abb103ab0852677c97b78ca12a39ba930"></a>
typedef <a class="el" href="classpcl_1_1search_1_1_search.html">pcl::search::Search</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>SearcherPtr</b></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="pri-attribs"></a>
Private 属性</h2></td></tr>
<tr class="memitem:a8addfb4ec55c04d29ef5624384ec23cf"><td class="memItemLeft" align="right" valign="top"><a id="a8addfb4ec55c04d29ef5624384ec23cf"></a>
SearcherPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a></td></tr>
<tr class="memdesc:a8addfb4ec55c04d29ef5624384ec23cf"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the spatial search object <br /></td></tr>
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<tr class="memitem:a7251600d738390348ac703fa3f91775b"><td class="memItemLeft" align="right" valign="top"><a id="a7251600d738390348ac703fa3f91775b"></a>
boost::function&lt; bool(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, float)&gt;&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">condition_function_</a></td></tr>
<tr class="memdesc:a7251600d738390348ac703fa3f91775b"><td class="mdescLeft">&#160;</td><td class="mdescRight">The condition function that needs to hold for clustering <br /></td></tr>
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<tr class="memitem:a9c73c915adcfbb7050bc9b804dde4c41"><td class="memItemLeft" align="right" valign="top"><a id="a9c73c915adcfbb7050bc9b804dde4c41"></a>
float&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a9c73c915adcfbb7050bc9b804dde4c41">cluster_tolerance_</a></td></tr>
<tr class="memdesc:a9c73c915adcfbb7050bc9b804dde4c41"><td class="mdescLeft">&#160;</td><td class="mdescRight">The distance to scan for cluster candidates (default = 0.0) <br /></td></tr>
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<tr class="memitem:a9cfb49d34b878c33fe3bcb5fa5895d24"><td class="memItemLeft" align="right" valign="top"><a id="a9cfb49d34b878c33fe3bcb5fa5895d24"></a>
int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">min_cluster_size_</a></td></tr>
<tr class="memdesc:a9cfb49d34b878c33fe3bcb5fa5895d24"><td class="mdescLeft">&#160;</td><td class="mdescRight">The minimum cluster size (default = 1) <br /></td></tr>
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int&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">max_cluster_size_</a></td></tr>
<tr class="memdesc:a498b8dc3314192260c359d7fff027a08"><td class="mdescLeft">&#160;</td><td class="mdescRight">The maximum cluster size (default = unlimited) <br /></td></tr>
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<tr class="memitem:a7a74b3c2c81f8b5b241bd1c6d4d0625c"><td class="memItemLeft" align="right" valign="top"><a id="a7a74b3c2c81f8b5b241bd1c6d4d0625c"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a></td></tr>
<tr class="memdesc:a7a74b3c2c81f8b5b241bd1c6d4d0625c"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if you want to be able to extract the clusters that are too large or too small (default = false) <br /></td></tr>
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<tr class="memitem:aaaceca7bf299bcf286f8f061683c93e1"><td class="memItemLeft" align="right" valign="top"><a id="aaaceca7bf299bcf286f8f061683c93e1"></a>
pcl::IndicesClustersPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">small_clusters_</a></td></tr>
<tr class="memdesc:aaaceca7bf299bcf286f8f061683c93e1"><td class="mdescLeft">&#160;</td><td class="mdescRight">The resultant clusters that contain less than min_cluster_size points <br /></td></tr>
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pcl::IndicesClustersPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">large_clusters_</a></td></tr>
<tr class="memdesc:a23fcf0249ffedd63a846e0a6196a2538"><td class="mdescLeft">&#160;</td><td class="mdescRight">The resultant clusters that contain more than max_cluster_size points <br /></td></tr>
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</table><table class="memberdecls">
<tr class="heading"><td colspan="2"><h2 class="groupheader"><a name="inherited"></a>
额外继承的成员函数</h2></td></tr>
<tr class="inherit_header pub_types_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pub_types_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Public 类型 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:ae2f6f6863a73337858b7a7a054eaae4f inherit pub_types_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ae2f6f6863a73337858b7a7a054eaae4f"></a>
typedef <a class="el" href="classpcl_1_1_point_cloud.html">pcl::PointCloud</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloud</b></td></tr>
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typedef PointCloud::Ptr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudPtr</b></td></tr>
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typedef PointCloud::ConstPtr&#160;</td><td class="memItemRight" valign="bottom"><b>PointCloudConstPtr</b></td></tr>
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typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesPtr</b></td></tr>
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<tr class="memitem:a51771056fb4ab8c448a11157acbe2ee0 inherit pub_types_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a51771056fb4ab8c448a11157acbe2ee0"></a>
typedef boost::shared_ptr&lt; <a class="el" href="structpcl_1_1_point_indices.html">PointIndices</a> const  &gt;&#160;</td><td class="memItemRight" valign="bottom"><b>PointIndicesConstPtr</b></td></tr>
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<tr class="inherit_header pro_methods_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_methods_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 成员函数 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top">bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">initCompute</a> ()</td></tr>
<tr class="memdesc:acceb20854934f4cf77e266eb5a44d4f0 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called before starting the actual computation.  <a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">更多...</a><br /></td></tr>
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<tr class="memitem:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="afc426c4eebb94b7734d4fa556bff1420"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ()</td></tr>
<tr class="memdesc:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">This method should get called after finishing the actual computation. <br /></td></tr>
<tr class="separator:afc426c4eebb94b7734d4fa556bff1420 inherit pro_methods_classpcl_1_1_p_c_l_base"><td class="memSeparator" colspan="2">&#160;</td></tr>
<tr class="inherit_header pro_attribs_classpcl_1_1_p_c_l_base"><td colspan="2" onclick="javascript:toggleInherit('pro_attribs_classpcl_1_1_p_c_l_base')"><img src="closed.png" alt="-"/>&#160;Protected 属性 继承自 <a class="el" href="classpcl_1_1_p_c_l_base.html">pcl::PCLBase&lt; PointT &gt;</a></td></tr>
<tr class="memitem:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="a09c70d8e06e3fb4f07903fe6f8d67869"></a>
PointCloudConstPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a></td></tr>
<tr class="memdesc:a09c70d8e06e3fb4f07903fe6f8d67869 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">The input point cloud dataset. <br /></td></tr>
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<tr class="memitem:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="aaee847c8a517ebf365bad2cb182a6626"></a>
IndicesPtr&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a></td></tr>
<tr class="memdesc:aaee847c8a517ebf365bad2cb182a6626 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">A pointer to the vector of point indices to use. <br /></td></tr>
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<tr class="memitem:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="ada1eadb824d34ca9206a86343d9760bb"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#ada1eadb824d34ca9206a86343d9760bb">use_indices_</a></td></tr>
<tr class="memdesc:ada1eadb824d34ca9206a86343d9760bb inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">Set to true if point indices are used. <br /></td></tr>
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<tr class="memitem:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="memItemLeft" align="right" valign="top"><a id="adadb0299f144528020ed558af6879662"></a>
bool&#160;</td><td class="memItemRight" valign="bottom"><a class="el" href="classpcl_1_1_p_c_l_base.html#adadb0299f144528020ed558af6879662">fake_indices_</a></td></tr>
<tr class="memdesc:adadb0299f144528020ed558af6879662 inherit pro_attribs_classpcl_1_1_p_c_l_base"><td class="mdescLeft">&#160;</td><td class="mdescRight">If no set of indices are given, we construct a set of fake indices that mimic the input <a class="el" href="classpcl_1_1_point_cloud.html" title="PointCloud represents the base class in PCL for storing collections of 3D points.">PointCloud</a>. <br /></td></tr>
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<a name="details" id="details"></a><h2 class="groupheader">详细描述</h2>
<div class="textblock"><h3>template&lt;typename PointT&gt;<br />
class pcl::ConditionalEuclideanClustering&lt; PointT &gt;</h3>

<p><b><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html" title="ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined c...">ConditionalEuclideanClustering</a></b> performs segmentation based on Euclidean distance and a user-defined clustering condition. </p>
<p>The condition that need to hold is currently passed using a function pointer. For more information check the documentation of <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa" title="Set the condition that needs to hold for neighboring points to be considered part of the same cluster...">setConditionFunction()</a> or the usage example below: </p><div class="fragment"><div class="line"><span class="keywordtype">bool</span></div>
<div class="line">enforceIntensitySimilarity (<span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_i.html">pcl::PointXYZI</a>&amp; point_a, <span class="keyword">const</span> <a class="code" href="structpcl_1_1_point_x_y_z_i.html">pcl::PointXYZI</a>&amp; point_b, <span class="keywordtype">float</span> squared_distance)</div>
<div class="line">{</div>
<div class="line">  <span class="keywordflow">if</span> (fabs (point_a.intensity - point_b.intensity) &lt; 0.1f)</div>
<div class="line">    <span class="keywordflow">return</span> (<span class="keyword">true</span>);</div>
<div class="line">  <span class="keywordflow">else</span></div>
<div class="line">    <span class="keywordflow">return</span> (<span class="keyword">false</span>);</div>
<div class="line">}</div>
<div class="line"><span class="comment">// ...</span></div>
<div class="line"><span class="comment">// Somewhere down to the main code</span></div>
<div class="line"><span class="comment">// ...</span></div>
<div class="line"><a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering&lt;pcl::PointXYZI&gt;</a> cec (<span class="keyword">true</span>);</div>
<div class="line">cec.setInputCloud (cloud_in);</div>
<div class="line">cec.setConditionFunction (&amp;enforceIntensitySimilarity);</div>
<div class="line"><span class="comment">// Points within this distance from one another are going to need to validate the enforceIntensitySimilarity function to be part of the same cluster:</span></div>
<div class="line">cec.setClusterTolerance (0.09f);</div>
<div class="line"><span class="comment">// Size constraints for the clusters:</span></div>
<div class="line">cec.setMinClusterSize (5);</div>
<div class="line">cec.setMaxClusterSize (30);</div>
<div class="line"><span class="comment">// The resulting clusters (an array of pointindices):</span></div>
<div class="line">cec.segment (*clusters);</div>
<div class="line"><span class="comment">// The clusters that are too small or too large in size can also be extracted separately:</span></div>
<div class="line">cec.getRemovedClusters (small_clusters, large_clusters);</div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a></div><div class="ttdoc">ConditionalEuclideanClustering performs segmentation based on Euclidean distance and a user-defined c...</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:84</div></div>
<div class="ttc" id="astructpcl_1_1_point_x_y_z_i_html"><div class="ttname"><a href="structpcl_1_1_point_x_y_z_i.html">pcl::PointXYZI</a></div><div class="ttdef"><b>Definition:</b> point_types.hpp:452</div></div>
</div><!-- fragment --> <dl class="section author"><dt>作者</dt><dd>Frits Florentinus </dd></dl>
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<h2 class="memtitle"><span class="permalink"><a href="#af707af3b42a9b8cd4314e95ac9134eed">&#9670;&nbsp;</a></span>ConditionalEuclideanClustering()</h2>

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          <td class="memname"><a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::<a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">ConditionalEuclideanClustering</a> </td>
          <td>(</td>
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<p>Constructor. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">extract_removed_clusters</td><td>Set to true if you want to be able to extract the clusters that are too large or too small (default = false) </td></tr>
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<div class="fragment"><div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;                                                                             :</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a> (),</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">condition_function_</a> (),</div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9c73c915adcfbb7050bc9b804dde4c41">cluster_tolerance_</a> (0.0f),</div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">min_cluster_size_</a> (1),</div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">max_cluster_size_</a> (std::numeric_limits&lt;int&gt;::max ()),</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a> (extract_removed_clusters),</div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">small_clusters_</a> (<span class="keyword">new</span> pcl::IndicesClusters),</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;          <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">large_clusters_</a> (<span class="keyword">new</span> pcl::IndicesClusters)</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160;      {</div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;      }</div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a23fcf0249ffedd63a846e0a6196a2538"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">pcl::ConditionalEuclideanClustering::large_clusters_</a></div><div class="ttdeci">pcl::IndicesClustersPtr large_clusters_</div><div class="ttdoc">The resultant clusters that contain more than max_cluster_size points</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:242</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a498b8dc3314192260c359d7fff027a08"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">pcl::ConditionalEuclideanClustering::max_cluster_size_</a></div><div class="ttdeci">int max_cluster_size_</div><div class="ttdoc">The maximum cluster size (default = unlimited)</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:233</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a7251600d738390348ac703fa3f91775b"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">pcl::ConditionalEuclideanClustering::condition_function_</a></div><div class="ttdeci">boost::function&lt; bool(const PointT &amp;, const PointT &amp;, float)&gt; condition_function_</div><div class="ttdoc">The condition function that needs to hold for clustering</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:224</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a7a74b3c2c81f8b5b241bd1c6d4d0625c"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">pcl::ConditionalEuclideanClustering::extract_removed_clusters_</a></div><div class="ttdeci">bool extract_removed_clusters_</div><div class="ttdoc">Set to true if you want to be able to extract the clusters that are too large or too small (default =...</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:236</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a8addfb4ec55c04d29ef5624384ec23cf"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">pcl::ConditionalEuclideanClustering::searcher_</a></div><div class="ttdeci">SearcherPtr searcher_</div><div class="ttdoc">A pointer to the spatial search object</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:221</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a9c73c915adcfbb7050bc9b804dde4c41"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a9c73c915adcfbb7050bc9b804dde4c41">pcl::ConditionalEuclideanClustering::cluster_tolerance_</a></div><div class="ttdeci">float cluster_tolerance_</div><div class="ttdoc">The distance to scan for cluster candidates (default = 0.0)</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:227</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_a9cfb49d34b878c33fe3bcb5fa5895d24"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">pcl::ConditionalEuclideanClustering::min_cluster_size_</a></div><div class="ttdeci">int min_cluster_size_</div><div class="ttdoc">The minimum cluster size (default = 1)</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:230</div></div>
<div class="ttc" id="aclasspcl_1_1_conditional_euclidean_clustering_html_aaaceca7bf299bcf286f8f061683c93e1"><div class="ttname"><a href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">pcl::ConditionalEuclideanClustering::small_clusters_</a></div><div class="ttdeci">pcl::IndicesClustersPtr small_clusters_</div><div class="ttdoc">The resultant clusters that contain less than min_cluster_size points</div><div class="ttdef"><b>Definition:</b> conditional_euclidean_clustering.h:239</div></div>
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<h2 class="groupheader">成员函数说明</h2>
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<h2 class="memtitle"><span class="permalink"><a href="#af264099e843fbf5d48049857e1e1d6af">&#9670;&nbsp;</a></span>getRemovedClusters()</h2>

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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::getRemovedClusters </td>
          <td>(</td>
          <td class="paramtype">IndicesClustersPtr &amp;&#160;</td>
          <td class="paramname"><em>small_clusters</em>, </td>
        </tr>
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          <td class="paramkey"></td>
          <td></td>
          <td class="paramtype">IndicesClustersPtr &amp;&#160;</td>
          <td class="paramname"><em>large_clusters</em>&#160;</td>
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          <td></td>
          <td>)</td>
          <td></td><td></td>
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<p>Get the clusters that are invalidated due to size constraints. </p>
<dl class="section note"><dt>注解</dt><dd>The constructor of this class needs to be initialized with true, and the segment method needs to have been called prior to using this method. </dd></dl>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">small_clusters</td><td>The resultant clusters that contain less than min_cluster_size points </td></tr>
    <tr><td class="paramdir">[out]</td><td class="paramname">large_clusters</td><td>The resultant clusters that contain more than max_cluster_size points </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00209"></a><span class="lineno">  209</span>&#160;      {</div>
<div class="line"><a name="l00210"></a><span class="lineno">  210</span>&#160;        <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a>)</div>
<div class="line"><a name="l00211"></a><span class="lineno">  211</span>&#160;        {</div>
<div class="line"><a name="l00212"></a><span class="lineno">  212</span>&#160;          PCL_WARN(<span class="stringliteral">&quot;[pcl::ConditionalEuclideanClustering::getRemovedClusters] You need to set extract_removed_clusters to true (in this class&#39; constructor) if you want to use this functionality.\n&quot;</span>);</div>
<div class="line"><a name="l00213"></a><span class="lineno">  213</span>&#160;          <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00214"></a><span class="lineno">  214</span>&#160;        }</div>
<div class="line"><a name="l00215"></a><span class="lineno">  215</span>&#160;        small_clusters = <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">small_clusters_</a>;</div>
<div class="line"><a name="l00216"></a><span class="lineno">  216</span>&#160;        large_clusters = <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">large_clusters_</a>;</div>
<div class="line"><a name="l00217"></a><span class="lineno">  217</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a8c03a476748bf42bbca42bc66141e785">&#9670;&nbsp;</a></span>segment()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::segment </td>
          <td>(</td>
          <td class="paramtype">pcl::IndicesClusters &amp;&#160;</td>
          <td class="paramname"><em>clusters</em></td><td>)</td>
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<p>Segment the input into separate clusters. </p>
<p>The input can be set using <a class="el" href="classpcl_1_1_p_c_l_base.html#a1952d7101f3942bac3b69ed55c1ca7ea" title="Provide a pointer to the input dataset">setInputCloud()</a> and <a class="el" href="classpcl_1_1_p_c_l_base.html#ab219359de6eb34c9d51e2e976dd1a0d1" title="Provide a pointer to the vector of indices that represents the input data.">setIndices()</a>. <br  />
 The size constraints for the resulting clusters can be set using <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a05a9b657c356141464cb80ee22ab53c2" title="Set the minimum number of points that a cluster needs to contain in order to be considered valid.">setMinClusterSize()</a> and <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a8c0ad8703fab1ddc50e09924eb2cfcc8" title="Set the maximum number of points that a cluster needs to contain in order to be considered valid.">setMaxClusterSize()</a>. <br  />
 The region growing parameters can be set using <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa" title="Set the condition that needs to hold for neighboring points to be considered part of the same cluster...">setConditionFunction()</a> and <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#abc0ffbe8624cf5c990484e35055f3c87" title="Set the spatial tolerance for new cluster candidates.">setClusterTolerance()</a>. <br  />
 </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[out]</td><td class="paramname">clusters</td><td>The resultant set of indices, indexing the points of the input cloud that correspond to the clusters </td></tr>
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<div class="fragment"><div class="line"><a name="l00044"></a><span class="lineno">   44</span>&#160;{</div>
<div class="line"><a name="l00045"></a><span class="lineno">   45</span>&#160;  <span class="comment">// Prepare output (going to use push_back)</span></div>
<div class="line"><a name="l00046"></a><span class="lineno">   46</span>&#160;  clusters.clear ();</div>
<div class="line"><a name="l00047"></a><span class="lineno">   47</span>&#160;  <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a>)</div>
<div class="line"><a name="l00048"></a><span class="lineno">   48</span>&#160;  {</div>
<div class="line"><a name="l00049"></a><span class="lineno">   49</span>&#160;    <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">small_clusters_</a>-&gt;clear ();</div>
<div class="line"><a name="l00050"></a><span class="lineno">   50</span>&#160;    <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">large_clusters_</a>-&gt;clear ();</div>
<div class="line"><a name="l00051"></a><span class="lineno">   51</span>&#160;  }</div>
<div class="line"><a name="l00052"></a><span class="lineno">   52</span>&#160; </div>
<div class="line"><a name="l00053"></a><span class="lineno">   53</span>&#160;  <span class="comment">// Validity checks</span></div>
<div class="line"><a name="l00054"></a><span class="lineno">   54</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">initCompute</a> () || <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;points.empty () || <a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>-&gt;empty () || !<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">condition_function_</a>)</div>
<div class="line"><a name="l00055"></a><span class="lineno">   55</span>&#160;    <span class="keywordflow">return</span>;</div>
<div class="line"><a name="l00056"></a><span class="lineno">   56</span>&#160; </div>
<div class="line"><a name="l00057"></a><span class="lineno">   57</span>&#160;  <span class="comment">// Initialize the search class</span></div>
<div class="line"><a name="l00058"></a><span class="lineno">   58</span>&#160;  <span class="keywordflow">if</span> (!<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a>)</div>
<div class="line"><a name="l00059"></a><span class="lineno">   59</span>&#160;  {</div>
<div class="line"><a name="l00060"></a><span class="lineno">   60</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;isOrganized ())</div>
<div class="line"><a name="l00061"></a><span class="lineno">   61</span>&#160;      <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a>.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_organized_neighbor.html">pcl::search::OrganizedNeighbor&lt;PointT&gt;</a> ());</div>
<div class="line"><a name="l00062"></a><span class="lineno">   62</span>&#160;    <span class="keywordflow">else</span></div>
<div class="line"><a name="l00063"></a><span class="lineno">   63</span>&#160;      <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a>.reset (<span class="keyword">new</span> <a class="code" href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree&lt;PointT&gt;</a> ());</div>
<div class="line"><a name="l00064"></a><span class="lineno">   64</span>&#160;  }</div>
<div class="line"><a name="l00065"></a><span class="lineno">   65</span>&#160;  <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a>-&gt;setInputCloud (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>, <a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>);</div>
<div class="line"><a name="l00066"></a><span class="lineno">   66</span>&#160; </div>
<div class="line"><a name="l00067"></a><span class="lineno">   67</span>&#160;  <span class="comment">// Temp variables used by search class</span></div>
<div class="line"><a name="l00068"></a><span class="lineno">   68</span>&#160;  std::vector&lt;int&gt; nn_indices;</div>
<div class="line"><a name="l00069"></a><span class="lineno">   69</span>&#160;  std::vector&lt;float&gt; nn_distances;</div>
<div class="line"><a name="l00070"></a><span class="lineno">   70</span>&#160; </div>
<div class="line"><a name="l00071"></a><span class="lineno">   71</span>&#160;  <span class="comment">// Create a bool vector of processed point indices, and initialize it to false</span></div>
<div class="line"><a name="l00072"></a><span class="lineno">   72</span>&#160;  <span class="comment">// Need to have it contain all possible points because radius search can not return indices into indices</span></div>
<div class="line"><a name="l00073"></a><span class="lineno">   73</span>&#160;  std::vector&lt;bool&gt; processed (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;points.size (), <span class="keyword">false</span>);</div>
<div class="line"><a name="l00074"></a><span class="lineno">   74</span>&#160; </div>
<div class="line"><a name="l00075"></a><span class="lineno">   75</span>&#160;  <span class="comment">// Process all points indexed by indices_</span></div>
<div class="line"><a name="l00076"></a><span class="lineno">   76</span>&#160;  <span class="keywordflow">for</span> (<span class="keywordtype">int</span> iii = 0; iii &lt; static_cast&lt;int&gt; (<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>-&gt;size ()); ++iii)  <span class="comment">// iii = input indices iterator</span></div>
<div class="line"><a name="l00077"></a><span class="lineno">   77</span>&#160;  {</div>
<div class="line"><a name="l00078"></a><span class="lineno">   78</span>&#160;    <span class="comment">// Has this point been processed before?</span></div>
<div class="line"><a name="l00079"></a><span class="lineno">   79</span>&#160;    <span class="keywordflow">if</span> ((*<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>)[iii] == -1 || processed[(*indices_)[iii]])</div>
<div class="line"><a name="l00080"></a><span class="lineno">   80</span>&#160;      <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00081"></a><span class="lineno">   81</span>&#160; </div>
<div class="line"><a name="l00082"></a><span class="lineno">   82</span>&#160;    <span class="comment">// Set up a new growing cluster</span></div>
<div class="line"><a name="l00083"></a><span class="lineno">   83</span>&#160;    std::vector&lt;int&gt; current_cluster;</div>
<div class="line"><a name="l00084"></a><span class="lineno">   84</span>&#160;    <span class="keywordtype">int</span> cii = 0;  <span class="comment">// cii = cluster indices iterator</span></div>
<div class="line"><a name="l00085"></a><span class="lineno">   85</span>&#160; </div>
<div class="line"><a name="l00086"></a><span class="lineno">   86</span>&#160;    <span class="comment">// Add the point to the cluster</span></div>
<div class="line"><a name="l00087"></a><span class="lineno">   87</span>&#160;    current_cluster.push_back ((*<a class="code" href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">indices_</a>)[iii]);</div>
<div class="line"><a name="l00088"></a><span class="lineno">   88</span>&#160;    processed[(*indices_)[iii]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00089"></a><span class="lineno">   89</span>&#160; </div>
<div class="line"><a name="l00090"></a><span class="lineno">   90</span>&#160;    <span class="comment">// Process the current cluster (it can be growing in size as it is being processed)</span></div>
<div class="line"><a name="l00091"></a><span class="lineno">   91</span>&#160;    <span class="keywordflow">while</span> (cii &lt; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (current_cluster.size ()))</div>
<div class="line"><a name="l00092"></a><span class="lineno">   92</span>&#160;    {</div>
<div class="line"><a name="l00093"></a><span class="lineno">   93</span>&#160;      <span class="comment">// Search for neighbors around the current seed point of the current cluster</span></div>
<div class="line"><a name="l00094"></a><span class="lineno">   94</span>&#160;      <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a8addfb4ec55c04d29ef5624384ec23cf">searcher_</a>-&gt;radiusSearch (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;points[current_cluster[cii]], <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9c73c915adcfbb7050bc9b804dde4c41">cluster_tolerance_</a>, nn_indices, nn_distances) &lt; 1)</div>
<div class="line"><a name="l00095"></a><span class="lineno">   95</span>&#160;      {</div>
<div class="line"><a name="l00096"></a><span class="lineno">   96</span>&#160;        cii++;</div>
<div class="line"><a name="l00097"></a><span class="lineno">   97</span>&#160;        <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00098"></a><span class="lineno">   98</span>&#160;      }</div>
<div class="line"><a name="l00099"></a><span class="lineno">   99</span>&#160; </div>
<div class="line"><a name="l00100"></a><span class="lineno">  100</span>&#160;      <span class="comment">// Process the neighbors</span></div>
<div class="line"><a name="l00101"></a><span class="lineno">  101</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> nii = 1; nii &lt; static_cast&lt;int&gt; (nn_indices.size ()); ++nii)  <span class="comment">// nii = neighbor indices iterator</span></div>
<div class="line"><a name="l00102"></a><span class="lineno">  102</span>&#160;      {</div>
<div class="line"><a name="l00103"></a><span class="lineno">  103</span>&#160;        <span class="comment">// Has this point been processed before?</span></div>
<div class="line"><a name="l00104"></a><span class="lineno">  104</span>&#160;        <span class="keywordflow">if</span> (nn_indices[nii] == -1 || processed[nn_indices[nii]])</div>
<div class="line"><a name="l00105"></a><span class="lineno">  105</span>&#160;          <span class="keywordflow">continue</span>;</div>
<div class="line"><a name="l00106"></a><span class="lineno">  106</span>&#160; </div>
<div class="line"><a name="l00107"></a><span class="lineno">  107</span>&#160;        <span class="comment">// Validate if condition holds</span></div>
<div class="line"><a name="l00108"></a><span class="lineno">  108</span>&#160;        <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">condition_function_</a> (<a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;points[current_cluster[cii]], <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;points[nn_indices[nii]], nn_distances[nii]))</div>
<div class="line"><a name="l00109"></a><span class="lineno">  109</span>&#160;        {</div>
<div class="line"><a name="l00110"></a><span class="lineno">  110</span>&#160;          <span class="comment">// Add the point to the cluster</span></div>
<div class="line"><a name="l00111"></a><span class="lineno">  111</span>&#160;          current_cluster.push_back (nn_indices[nii]);</div>
<div class="line"><a name="l00112"></a><span class="lineno">  112</span>&#160;          processed[nn_indices[nii]] = <span class="keyword">true</span>;</div>
<div class="line"><a name="l00113"></a><span class="lineno">  113</span>&#160;        }</div>
<div class="line"><a name="l00114"></a><span class="lineno">  114</span>&#160;      }</div>
<div class="line"><a name="l00115"></a><span class="lineno">  115</span>&#160;      cii++;</div>
<div class="line"><a name="l00116"></a><span class="lineno">  116</span>&#160;    }</div>
<div class="line"><a name="l00117"></a><span class="lineno">  117</span>&#160; </div>
<div class="line"><a name="l00118"></a><span class="lineno">  118</span>&#160;    <span class="comment">// If extracting removed clusters, all clusters need to be saved, otherwise only the ones within the given cluster size range</span></div>
<div class="line"><a name="l00119"></a><span class="lineno">  119</span>&#160;    <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a> ||</div>
<div class="line"><a name="l00120"></a><span class="lineno">  120</span>&#160;        (<span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (current_cluster.size ()) &gt;= <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">min_cluster_size_</a> &amp;&amp;</div>
<div class="line"><a name="l00121"></a><span class="lineno">  121</span>&#160;         <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (current_cluster.size ()) &lt;= <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">max_cluster_size_</a>))</div>
<div class="line"><a name="l00122"></a><span class="lineno">  122</span>&#160;    {</div>
<div class="line"><a name="l00123"></a><span class="lineno">  123</span>&#160;      <a class="code" href="structpcl_1_1_point_indices.html">pcl::PointIndices</a> pi;</div>
<div class="line"><a name="l00124"></a><span class="lineno">  124</span>&#160;      pi.header = <a class="code" href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">input_</a>-&gt;header;</div>
<div class="line"><a name="l00125"></a><span class="lineno">  125</span>&#160;      pi.indices.resize (current_cluster.size ());</div>
<div class="line"><a name="l00126"></a><span class="lineno">  126</span>&#160;      <span class="keywordflow">for</span> (<span class="keywordtype">int</span> ii = 0; ii &lt; static_cast&lt;int&gt; (current_cluster.size ()); ++ii)  <span class="comment">// ii = indices iterator</span></div>
<div class="line"><a name="l00127"></a><span class="lineno">  127</span>&#160;        pi.indices[ii] = current_cluster[ii];</div>
<div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160; </div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;      if (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a> &amp;&amp; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (current_cluster.size ()) &lt; <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">min_cluster_size_</a>)</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#aaaceca7bf299bcf286f8f061683c93e1">small_clusters_</a>-&gt;push_back (pi);</div>
<div class="line"><a name="l00131"></a><span class="lineno">  131</span>&#160;      <span class="keywordflow">else</span> <span class="keywordflow">if</span> (<a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7a74b3c2c81f8b5b241bd1c6d4d0625c">extract_removed_clusters_</a> &amp;&amp; <span class="keyword">static_cast&lt;</span><span class="keywordtype">int</span><span class="keyword">&gt;</span> (current_cluster.size ()) &gt; <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">max_cluster_size_</a>)</div>
<div class="line"><a name="l00132"></a><span class="lineno">  132</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a23fcf0249ffedd63a846e0a6196a2538">large_clusters_</a>-&gt;push_back (pi);</div>
<div class="line"><a name="l00133"></a><span class="lineno">  133</span>&#160;      <span class="keywordflow">else</span></div>
<div class="line"><a name="l00134"></a><span class="lineno">  134</span>&#160;        clusters.push_back (pi);</div>
<div class="line"><a name="l00135"></a><span class="lineno">  135</span>&#160;    }</div>
<div class="line"><a name="l00136"></a><span class="lineno">  136</span>&#160;  }</div>
<div class="line"><a name="l00137"></a><span class="lineno">  137</span>&#160; </div>
<div class="line"><a name="l00138"></a><span class="lineno">  138</span>&#160;  <a class="code" href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">deinitCompute</a> ();</div>
<div class="line"><a name="l00139"></a><span class="lineno">  139</span>&#160;}</div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_a09c70d8e06e3fb4f07903fe6f8d67869"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#a09c70d8e06e3fb4f07903fe6f8d67869">pcl::PCLBase::input_</a></div><div class="ttdeci">PointCloudConstPtr input_</div><div class="ttdoc">The input point cloud dataset.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:150</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_aaee847c8a517ebf365bad2cb182a6626"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#aaee847c8a517ebf365bad2cb182a6626">pcl::PCLBase::indices_</a></div><div class="ttdeci">IndicesPtr indices_</div><div class="ttdoc">A pointer to the vector of point indices to use.</div><div class="ttdef"><b>Definition:</b> pcl_base.h:153</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_acceb20854934f4cf77e266eb5a44d4f0"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#acceb20854934f4cf77e266eb5a44d4f0">pcl::PCLBase::initCompute</a></div><div class="ttdeci">bool initCompute()</div><div class="ttdoc">This method should get called before starting the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:139</div></div>
<div class="ttc" id="aclasspcl_1_1_p_c_l_base_html_afc426c4eebb94b7734d4fa556bff1420"><div class="ttname"><a href="classpcl_1_1_p_c_l_base.html#afc426c4eebb94b7734d4fa556bff1420">pcl::PCLBase::deinitCompute</a></div><div class="ttdeci">bool deinitCompute()</div><div class="ttdoc">This method should get called after finishing the actual computation.</div><div class="ttdef"><b>Definition:</b> pcl_base.hpp:174</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_kd_tree_html"><div class="ttname"><a href="classpcl_1_1search_1_1_kd_tree.html">pcl::search::KdTree</a></div><div class="ttdoc">search::KdTree is a wrapper class which inherits the pcl::KdTree class for performing search function...</div><div class="ttdef"><b>Definition:</b> kdtree.h:63</div></div>
<div class="ttc" id="aclasspcl_1_1search_1_1_organized_neighbor_html"><div class="ttname"><a href="classpcl_1_1search_1_1_organized_neighbor.html">pcl::search::OrganizedNeighbor</a></div><div class="ttdoc">OrganizedNeighbor is a class for optimized nearest neigbhor search in organized point clouds.</div><div class="ttdef"><b>Definition:</b> organized.h:63</div></div>
<div class="ttc" id="astructpcl_1_1_point_indices_html"><div class="ttname"><a href="structpcl_1_1_point_indices.html">pcl::PointIndices</a></div><div class="ttdef"><b>Definition:</b> PointIndices.h:13</div></div>
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<h2 class="memtitle"><span class="permalink"><a href="#abc0ffbe8624cf5c990484e35055f3c87">&#9670;&nbsp;</a></span>setClusterTolerance()</h2>

<div class="memitem">
<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setClusterTolerance </td>
          <td>(</td>
          <td class="paramtype">float&#160;</td>
          <td class="paramname"><em>cluster_tolerance</em></td><td>)</td>
          <td></td>
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<p>Set the spatial tolerance for new cluster candidates. </p>
<p>Any two points within this distance from one another will need to evaluate a certain condition in order to be made part of the same cluster. The condition can be set using <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#a62bf691a0260fc34c3df9d9d48cb2faa" title="Set the condition that needs to hold for neighboring points to be considered part of the same cluster...">setConditionFunction()</a>. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">cluster_tolerance</td><td>The distance to scan for cluster candidates (default = 0.0) </td></tr>
  </table>
  </dd>
</dl>
<div class="fragment"><div class="line"><a name="l00147"></a><span class="lineno">  147</span>&#160;      {</div>
<div class="line"><a name="l00148"></a><span class="lineno">  148</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9c73c915adcfbb7050bc9b804dde4c41">cluster_tolerance_</a> = cluster_tolerance;</div>
<div class="line"><a name="l00149"></a><span class="lineno">  149</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a62bf691a0260fc34c3df9d9d48cb2faa">&#9670;&nbsp;</a></span>setConditionFunction()</h2>

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<div class="memproto">
<div class="memtemplate">
template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setConditionFunction </td>
          <td>(</td>
          <td class="paramtype">bool(*)(const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, const <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &amp;, float)&#160;</td>
          <td class="paramname"><em>condition_function</em></td><td>)</td>
          <td></td>
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<p>Set the condition that needs to hold for neighboring points to be considered part of the same cluster. </p>
<p>Any two points within a certain distance from one another will need to evaluate this condition in order to be made part of the same cluster. The distance can be set using <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html#abc0ffbe8624cf5c990484e35055f3c87" title="Set the spatial tolerance for new cluster candidates.">setClusterTolerance()</a>. <br  />
 Note that for a point to be part of a cluster, the condition only needs to hold for at least 1 point pair. To clarify, the following statement is false: Any two points within a cluster always evaluate this condition function to true. <br  />
<br  />
 The input arguments of the condition function are: </p><ul>
<li>
PointT The first point of the point pair </li>
<li>
PointT The second point of the point pair </li>
<li>
float The squared distance between the points </li>
</ul>
<p>The output argument is a boolean, returning true will merge the second point into the cluster of the first point. </p><dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">condition_function</td><td>The condition function that needs to hold for clustering </td></tr>
  </table>
  </dd>
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<div class="fragment"><div class="line"><a name="l00128"></a><span class="lineno">  128</span>&#160;      {</div>
<div class="line"><a name="l00129"></a><span class="lineno">  129</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a7251600d738390348ac703fa3f91775b">condition_function_</a> = condition_function;</div>
<div class="line"><a name="l00130"></a><span class="lineno">  130</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a8c0ad8703fab1ddc50e09924eb2cfcc8">&#9670;&nbsp;</a></span>setMaxClusterSize()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setMaxClusterSize </td>
          <td>(</td>
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          <td class="paramname"><em>max_cluster_size</em></td><td>)</td>
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<p>Set the maximum number of points that a cluster needs to contain in order to be considered valid. </p>
<dl class="params"><dt>参数</dt><dd>
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    <tr><td class="paramdir">[in]</td><td class="paramname">max_cluster_size</td><td>The maximum cluster size (default = unlimited) </td></tr>
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<div class="fragment"><div class="line"><a name="l00179"></a><span class="lineno">  179</span>&#160;      {</div>
<div class="line"><a name="l00180"></a><span class="lineno">  180</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a498b8dc3314192260c359d7fff027a08">max_cluster_size_</a> = max_cluster_size;</div>
<div class="line"><a name="l00181"></a><span class="lineno">  181</span>&#160;      }</div>
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<h2 class="memtitle"><span class="permalink"><a href="#a05a9b657c356141464cb80ee22ab53c2">&#9670;&nbsp;</a></span>setMinClusterSize()</h2>

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template&lt;typename PointT &gt; </div>
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          <td class="memname">void <a class="el" href="classpcl_1_1_conditional_euclidean_clustering.html">pcl::ConditionalEuclideanClustering</a>&lt; <a class="el" href="structpcl_1_1_point_x_y_z_r_g_b_a.html">PointT</a> &gt;::setMinClusterSize </td>
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<p>Set the minimum number of points that a cluster needs to contain in order to be considered valid. </p>
<dl class="params"><dt>参数</dt><dd>
  <table class="params">
    <tr><td class="paramdir">[in]</td><td class="paramname">min_cluster_size</td><td>The minimum cluster size (default = 1) </td></tr>
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<div class="fragment"><div class="line"><a name="l00163"></a><span class="lineno">  163</span>&#160;      {</div>
<div class="line"><a name="l00164"></a><span class="lineno">  164</span>&#160;        <a class="code" href="classpcl_1_1_conditional_euclidean_clustering.html#a9cfb49d34b878c33fe3bcb5fa5895d24">min_cluster_size_</a> = min_cluster_size;</div>
<div class="line"><a name="l00165"></a><span class="lineno">  165</span>&#160;      }</div>
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<hr/>该类的文档由以下文件生成:<ul>
<li>segmentation/include/pcl/segmentation/<a class="el" href="conditional__euclidean__clustering_8h_source.html">conditional_euclidean_clustering.h</a></li>
<li>segmentation/include/pcl/segmentation/impl/<a class="el" href="conditional__euclidean__clustering_8hpp_source.html">conditional_euclidean_clustering.hpp</a></li>
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